Why Amazon's Ratings Might Mislead You: The Story of Herding Effects

By Ting Wang, Dashun Wang. 2014

24 citations recorded by the source corpus.

Abstract Our society is increasingly relying on digitalized, aggregated opinions of individuals to make decisions (e.g., product recommendation based on collective ratings). One key requirement of harnessing this “wisdom of crowd” is the independency of individuals' opinions; yet, in real settings, collective opinions are rarely simple aggregations of independent minds. Recent experimental studies document that disclosing prior collective ratings distorts individuals' decision making as well as their perceptions of quality and value, highlighting a fundamental discrepancy between our perceived values from collective ratings and products' intrinsic values. Here we present a mechanistic framework to describe herding effects of prior collective ratings on subsequent individual decision making. Using large-scale longitudinal customer rating datasets, we find that our method successfully captures the dynamics of ratings growth, helping us separate social influence bias from inherent values. Leveraging the proposed framework, we quantitatively characterize the herding effects existing in product rating systems and promote strategies to untangle manipulations and social biases.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science, Recommender Systems, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Meng Wang
5. Tat-seng Chua
6. Jiawei Chen
7. Ricardo A. Baeza-Yates
8. Hande Dong

In Computer Science Applications

1. Xiangnan He
2. Ricardo A. Baeza-Yates

In Sociology and Political Science

1. Ricardo A. Baeza-Yates
2. Jiawei Chen
3. Hande Dong
4. Xiang Wang
5. Fuli Feng
6. Meng Wang
7. Xiangnan He

In Recommender Systems

1. Xiang Wang
2. Fuli Feng
3. Meng Wang
4. Ricardo A. Baeza-Yates
5. Xiangnan He
6. Tat-seng Chua

In Social Sciences & Humanities

1. Ricardo A. Baeza-Yates

Citations in the corpus

• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “ing values do not always signify user true preference. For example, influenced by high ratings of public comments on an item, one user is highly likely to change her low rate, avoiding being too harsh [22], [23]. Such phenomenon of conformity is common and cause biases in user ratings. As shown in Kr”
• Bias on the web (2018, background): “Such bias has been explored in the context of Amazon reviews data 26 and is often referred to as “social conformity,” or “the herding effect.””